What question did this study set out to answer?

This research aims to enhance control methods for autonomous electric vehicles driven by PMSMs, specifically targeting torque control and trajectory tracking.

February 19, 2026Open Access

Adaptive linear MPC for a PMSM-driven autonomous EV with a filtered third-order generalized integrator observer

Puntos clave

This research aims to enhance control methods for autonomous electric vehicles driven by PMSMs, specifically targeting torque control and trajectory tracking.
Developed an adaptive linear MPC strategy integrating a filtered flux observer.
Formulated a nine-state predictive model using Taylor-series-based linearization.
Implemented an active-set quadratic programming optimizer for voltage and steering angle calculations.
Achieved 99.9% reduction in yaw mean absolute error (MAE) compared to classical MPC.
Attained 65% reduction in lateral position root mean square error.
Reduced steering signal variation by 93% under varying velocity conditions.

Resumen

Autonomous electric vehicles require precise coordination between motor torque control and vehicle trajectory tracking. However, permanent magnet synchronous motors (PMSMs) exhibit nonlinear behavior-particularly inductance variation and flux weakening-that conventional model predictive control (MPC) methods with fixed parameters cannot adequately capture, leading to degraded tracking performance during dynamic transitions. To address these challenges, this paper proposes an adaptive linear MPC (AL-MPC) strategy that integrates three key components. First, a moving-average-filtered third-order generalized integrator flux observer provides real-time estimation of electromagnetic torque and d-q axis stator reactance, enabling the predictive model to adapt to operating-point-dependent PMSM nonlinearities. Second, a Taylor-series-based linearization formulates a unified nine-state predictive model coupling motor currents, wheel velocity, yaw angle, and lateral position, which is updated at each sampling instant to reflect current operating conditions. Third, an active-set quadratic programming optimizer efficiently computes optimal d-q voltages and steering angle while enforcing current, voltage, and state constraints. The AL-MPC is validated through MATLAB/Simulink simulations and hardware-in-the-loop (HIL) testing on a TI C2000 embedded controller. Compared with classical seven-dimensional linear MPC, the proposed method achieves 99.9% reduction in yaw mean absolute error (MAE), 65% reduction in lateral position root mean square error, and 93% reduction in steering signal variation under varying velocity conditions. Against adaptive nonlinear MPC, it attains 77.7% lower yaw MAE, 94.6% lower lateral MAE, 95.4% reduction in velocity ripple, and 67% lower voltage ripple during rapid acceleration with torque disturbances, while requiring 3.7% less computation time. The HIL results confirm real-time feasibility with a total execution time of 9.65 ms per control cycle.

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